Imagery gathered by a Focal Plane Array (FPA) based sensor often suffers from the intrinsic non-uniform response of the individual detectors of the FPA. A digital Non-Uniformity Correction (NUC) can compensate for this distortion by implementing a functional transformation to the numerical output of each digitized FPA pixel. Such a NUC is often measured by exposing the sensor to one or more sources of uniform flux, and computed so that the post-NUC image of such uniform scenery has minimal spatial variation.
Alternative NUC implementations adopt a scene-adaptive approach , using only the data in the gathered video sequence for which one wants to NUC. Several implementations, such as temporal high-pass filtering, neural-network, steepest-descent, or adaptive LMS, fundamentally depend on scene-predicted image necessary compute the appropriate functional correction. Such predicted images are invariably a spatial transformation of a single frame of video; it is because of the limited accuracy of such a single-image prediction that mandates algorithm compromises between slow convergence and pathological collapses, such as image scene burn-in or image washout.
Previously reported research in image resolution enhancement mandates the construction of a Temporal Accumulation of Registered Image Data (TARID) composite image as a pre-processing step. Such TARID composite images have significantly improved accuracy and robustness over any single-frame predicted image applied to scene adaptive NUC algorithms, resulting in markedly improved performance in both convergence and stability.